diff --git a/lab_4/datasetlab1/marketing_campaign.csv b/lab_4/datasetlab1/marketing_campaign.csv
new file mode 100644
index 0000000..ecf0643
--- /dev/null
+++ b/lab_4/datasetlab1/marketing_campaign.csv
@@ -0,0 +1,2241 @@
+ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
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+5710 1970 Graduation Together 40548 0 1 10-10-2012 31 110 0 5 2 0 3 2 2 1 4 5 0 1 0 0 0 0 3 11 0
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+8430 1957 Graduation Together 21994 0 1 24-12-2012 4 9 0 6 3 1 3 1 0 0 3 5 0 0 0 0 0 0 3 11 0
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+6312 1959 Graduation Married 65031 0 1 17-03-2013 29 258 107 291 84 37 86 4 10 3 13 7 0 0 0 0 0 0 3 11 0
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+1859 1972 Master Married 75251 0 0 27-08-2012 34 721 111 925 97 18 18 1 7 6 5 5 0 0 0 0 0 0 3 11 1
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+5268 1960 Graduation Married 29440 1 1 11-08-2013 95 17 8 14 10 4 3 2 1 0 4 7 0 0 0 0 0 0 3 11 0
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+9215 1980 PhD Married 43974 1 0 12-12-2012 19 378 0 101 0 0 72 5 6 4 6 7 0 0 0 0 0 0 3 11 0
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diff --git a/lab_4/datasetlab1/marketing_campaign2.csv b/lab_4/datasetlab1/marketing_campaign2.csv
new file mode 100644
index 0000000..ecf0643
--- /dev/null
+++ b/lab_4/datasetlab1/marketing_campaign2.csv
@@ -0,0 +1,2241 @@
+ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
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+9405 1954 PhD Married 52869 1 1 15-10-2012 40 84 3 61 2 1 21 3 3 1 4 7 0 0 0 0 0 0 3 11 1
diff --git a/lab_4/lab4.ipynb b/lab_4/lab4.ipynb
new file mode 100644
index 0000000..9435798
--- /dev/null
+++ b/lab_4/lab4.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Lab 4 Malafeev PIbd-31**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1.Для начала выберем бизнес-цели для задач регрессии и классификации."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Классификация. Цель: определить, откликнется ли клиент на маркетинговую кампанию. Столбец целевой переменной - Response, 1 - откликнулся, 0 - нет. Признаки - Возраст, Уровень дохода. (Age, Income)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Регрессия. Цель: прогноз расходов клиента. Столбец целевой переменной: Total_Spending - общие расходы, будут считаться по всем расходам. Признаки такие же."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "2.Достижимый уровень качества:\n",
+ "Классификация:\n",
+ "Оценка метрики accuracy: ориентир 70-80% (с учетом ограниченных признаков).\n",
+ "Регрессия:\n",
+ "MSE (среднеквадратичная ошибка): минимизация, ориентир в зависимости от разброса целевой переменной.\n",
+ "R^2 > 0.6"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "3.Ориентир. Классификация:\n",
+ "DummyClassifier, предсказывающий самый частый класс, даст accuracy ~50-60%.\n",
+ "Регрессия:\n",
+ "Прогноз среднего значения целевой переменной."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X_class_train: (1568, 2), y_class_train: (1568,)\n",
+ "X_reg_train: (1568, 2), y_reg_train: (1568,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "data = pd.read_csv(\".//datasetlab1//marketing_campaign.csv\", sep=\"\\t\")\n",
+ "data2 = pd.read_csv(\".//datasetlab1//marketing_campaign2.csv\", sep=\"\\t\")\n",
+ "\n",
+ "# Преобразуем данные для классификации (дата для отклика на кампанию)\n",
+ "data['Age'] = 2024 - data['Year_Birth'] \n",
+ "data = data[['Age', 'Income', 'Response']] \n",
+ "\n",
+ "X_class = data[['Age', 'Income']]\n",
+ "y_class = data['Response']\n",
+ "\n",
+ "# Преобразуем данные для регрессии (прогноз расходов)\n",
+ "data2['Age'] = 2024 - data2['Year_Birth'] \n",
+ "data2['Total_Spending'] = (data2['MntWines'] + data2['MntFruits'] + data2['MntMeatProducts'] +\n",
+ " data2['MntFishProducts'] + data2['MntSweetProducts'] + data2['MntGoldProds'])\n",
+ "data2 = data2[['Age', 'Income', 'Total_Spending']] \n",
+ "\n",
+ "# Разделение на признаки и целевую переменную для регрессии\n",
+ "X_reg = data2[['Age', 'Income']]\n",
+ "y_reg = data2['Total_Spending']\n",
+ "\n",
+ "# Масштабирование данных\n",
+ "scaler = StandardScaler()\n",
+ "X_class_scaled = scaler.fit_transform(X_class)\n",
+ "X_reg_scaled = scaler.fit_transform(X_reg)\n",
+ "\n",
+ "# Разделение на тренировочные и тестовые выборки\n",
+ "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class_scaled, y_class, test_size=0.3, random_state=42)\n",
+ "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg_scaled, y_reg, test_size=0.3, random_state=42)\n",
+ "\n",
+ "# Проверим, что все выглядит правильно\n",
+ "print(f\"X_class_train: {X_train_class.shape}, y_class_train: {y_train_class.shape}\")\n",
+ "print(f\"X_reg_train: {X_train_reg.shape}, y_reg_train: {y_train_reg.shape}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "5-6.Выбор трёх моделей и построение конвейера"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Logistic Regression - Средняя точность модели: 0.8475 ± 0.0027\n",
+ "Random Forest - Средняя точность модели: 0.8258 ± 0.0099\n",
+ "SVM - Средняя точность модели: 0.8529 ± 0.0027\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "\n",
+ "# Удаляем строки с пропущенными значениями\n",
+ "X_class_scaled = X_class.dropna()\n",
+ "y_class = y_class[X_class_scaled.index]\n",
+ "\n",
+ "models = [\n",
+ " ('Logistic Regression', LogisticRegression(max_iter=1000)),\n",
+ " ('Random Forest', RandomForestClassifier(n_estimators=100)),\n",
+ " ('SVM', SVC())\n",
+ "]\n",
+ "\n",
+ "# Создаем конвейер\n",
+ "imputer = SimpleImputer(strategy='mean') \n",
+ "scaler = StandardScaler()\n",
+ "\n",
+ "for name, model in models:\n",
+ " pipe = Pipeline([\n",
+ " ('imputer', imputer),\n",
+ " ('scaler', scaler),\n",
+ " ('classifier', model)\n",
+ " ])\n",
+ " \n",
+ " scores = cross_val_score(pipe, X_class_scaled, y_class, cv=5, scoring='accuracy')\n",
+ " print(f\"{name} - Средняя точность модели: {scores.mean():.4f} ± {scores.std():.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Вот такие модели и конвейр я выбрал: Imputer: Заполняет пропущенные значения средним (если они есть).\n",
+ "Scaler: Масштабирует данные с помощью StandardScaler.\n",
+ "Classifier: Используются три модели:\n",
+ "LogisticRegression: Логистическая регрессия.\n",
+ "RandomForestClassifier: Случайный лес.\n",
+ "SVC: Метод опорных векторов (SVM)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "7.Теперь сделаем настройку гиперпараметров."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
+ "Logistic Regression - Лучшие гиперпараметры: {'classifier__C': 0.1, 'classifier__solver': 'lbfgs'}\n",
+ "Logistic Regression - Лучшая точность: 0.8484\n",
+ "--------------------------------------------------\n",
+ "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
+ "Random Forest - Лучшие гиперпараметры: {'classifier__max_depth': 10, 'classifier__n_estimators': 100}\n",
+ "Random Forest - Лучшая точность: 0.8520\n",
+ "--------------------------------------------------\n",
+ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
+ "SVM - Лучшие гиперпараметры: {'classifier__C': 1, 'classifier__kernel': 'rbf'}\n",
+ "SVM - Лучшая точность: 0.8529\n",
+ "--------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import GridSearchCV\n",
+ "models = [\n",
+ " ('Logistic Regression', LogisticRegression(max_iter=1000), {'classifier__C': [0.1, 1, 10], 'classifier__solver': ['lbfgs', 'liblinear']}),\n",
+ " ('Random Forest', RandomForestClassifier(n_estimators=100), {'classifier__n_estimators': [50, 100, 200], 'classifier__max_depth': [10, 20, None]}),\n",
+ " ('SVM', SVC(), {'classifier__C': [0.1, 1, 10], 'classifier__kernel': ['linear', 'rbf']})\n",
+ "]\n",
+ "\n",
+ "for name, model, param_grid in models:\n",
+ " pipe = Pipeline([\n",
+ " ('imputer', imputer),\n",
+ " ('scaler', scaler),\n",
+ " ('classifier', model)\n",
+ " ])\n",
+ " \n",
+ " grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n",
+ " grid_search.fit(X_class_scaled, y_class)\n",
+ "\n",
+ " print(f\"{name} - Лучшие гиперпараметры: {grid_search.best_params_}\")\n",
+ " print(f\"{name} - Лучшая точность: {grid_search.best_score_:.4f}\")\n",
+ " print(\"-\" * 50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Тут мы проходим по моделям и настраиваем гиперпараметры с помощью GridSearchCV с помощью кросс-валидации. Параметры: cv=5: 5 фолдов для кросс-валидации.\n",
+ "scoring='accuracy': Мы используем точность как метрику.\n",
+ "n_jobs=-1: Используем все доступные процессоры для ускорения вычислений.\n",
+ "verbose=1: Подробный вывод процесса."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "8.Обучим модели"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
+ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
+ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
+ ]
+ }
+ ],
+ "source": [
+ "best_models = {} \n",
+ "for name, model, param_grid in models: \n",
+ " grid_search.fit(X_class_scaled, y_class)\n",
+ " best_models[name] = grid_search.best_estimator_ \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "9.Оценим модели."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
+ "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+ "\n",
+ "# Оценка качества классификации\n",
+ "for name, model in best_models.items():\n",
+ " y_pred_class = model.predict(X_class_scaled) # Предсказание для классификации\n",
+ "\n",
+ "# Оценка качества регрессии\n",
+ "for name, model in best_models.items():\n",
+ " y_pred_reg = model.predict(X_reg_scaled) # Предсказание для регрессии\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Т.к. вывод слишком длинный, приложу его тут(вылазит Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...):
Оценка качества для модели Logistic Regression:
\n",
+ "Accuracy: 0.8528880866425993
\n",
+ "Precision: 0.8181818181818182
\n",
+ "Recall: 0.02702702702702703
\n",
+ "F1-Score: 0.05232558139534884
\n",
+ "ROC AUC: Не поддерживается для этой модели
\n",
+ "
\n",
+ "
\n",
+ "Оценка качества для модели Random Forest:
\n",
+ "Accuracy: 0.8528880866425993
\n",
+ "Precision: 0.8181818181818182
\n",
+ "Recall: 0.02702702702702703
\n",
+ "F1-Score: 0.05232558139534884
\n",
+ "ROC AUC: Не поддерживается для этой модели
\n",
+ "
\n",
+ "
\n",
+ "Оценка качества для модели SVM:
\n",
+ "Accuracy: 0.8528880866425993
\n",
+ "Precision: 0.8181818181818182
\n",
+ "Recall: 0.02702702702702703
\n",
+ "F1-Score: 0.05232558139534884
\n",
+ "ROC AUC: Не поддерживается для этой модели
\n",
+ "
\n",
+ "
Задача регрессии:
\n",
+ "Оценка качества для модели Logistic Regression:
\n",
+ "MAE: 605.7982142857143
\n",
+ "MSE: 729533.7598214286
\n",
+ "RMSE: 854.1274845252485
\n",
+ "R²: -1.0122722045012051
\n",
+ "
\n",
+ "
\n",
+ "Оценка качества для модели Random Forest:
\n",
+ "MAE: 605.7982142857143
\n",
+ "MSE: 729533.7598214286
\n",
+ "RMSE: 854.1274845252485
\n",
+ "R²: -1.0122722045012051
\n",
+ "
\n",
+ "
\n",
+ "Оценка качества для модели SVM:
\n",
+ "MAE: 605.7982142857143
\n",
+ "MSE: 729533.7598214286
\n",
+ "RMSE: 854.1274845252485
\n",
+ "R²: -1.0122722045012051
\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Почему выбрал эти метирки:
Классификация (Отклик на предложение)
\n",
+ "Целевая переменная — бинарная (0 и 1), где 1 — откликнулся, а 0 — не откликнулся. Для классификации подходящими метриками являются:
\n",
+ "
\n",
+ "Accuracy (Точность):\n",
+ "Это доля правильно классифицированных объектов среди всех. \n",
+ "Подходит для оценки общей эффективности модели. Однако важно учитывать, что если классы несбалансированы, точность может быть обманчивой.
\n",
+ "Precision (Точность):\n",
+ "\n",
+ "Это доля истинных положительных случаев среди всех предсказанных положительных случаев.\n",
+ "Важна для задач, где важно минимизировать количество ложных срабатываний, например, когда модель ошибочно классифицирует клиента как откликнувшегося (True Positive).
\n",
+ "Recall (Полнота):\n",
+ "\n",
+ "Это доля истинных положительных случаев среди всех истинных положительных случаев.\n",
+ "Важно для задач, где важно не пропустить откликнувшихся клиентов (False Negatives).
\n",
+ "F1-Score:\n",
+ "\n",
+ "Это гармоническое среднее между точностью и полнотой.\n",
+ "Подходит для оценки моделей в случаях, когда важно иметь баланс между точностью и полнотой, особенно в ситуациях с несбалансированными классами.
\n",
+ "ROC AUC:\n",
+ "Площадь под кривой ROC, которая отображает способность модели различать положительные и отрицательные классы.\n",
+ "Чем выше значение AUC, тем лучше модель справляется с разделением классов."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Регрессия (Прогноз расходов)
\n",
+ "Целевая переменная — это числовое значение (расходы клиента). Для задач регрессии используются другие метрики:
\n",
+ "\n",
+ "Mean Absolute Error (MAE):\n",
+ "\n",
+ "Это средняя абсолютная ошибка предсказания.\n",
+ "Простой и интерпретируемый показатель, который описывает среднее отклонение предсказанных значений от фактических.
\n",
+ "Mean Squared Error (MSE):\n",
+ "\n",
+ "Это средняя квадратичная ошибка.\n",
+ "Чувствителен к большим ошибкам, так как квадратичный штраф увеличивает вес больших отклонений, что полезно, если вы хотите минимизировать большие ошибки.
\n",
+ "Root Mean Squared Error (RMSE):\n",
+ "\n",
+ "Это квадратный корень из MSE.\n",
+ "Подходит для задач, где важно учитывать большие ошибки, так как более чувствителен к выбросам.
\n",
+ "R-squared (R²):\n",
+ "\n",
+ "Это коэффициент детерминации, который показывает, какая доля дисперсии целевой переменной объясняется моделью.\n",
+ "R² может быть полезен для оценки того, насколько хорошо модель объясняет вариацию целевой переменной, но не всегда подходит, если модель имеет много выбросов или некорректно подогнана.
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "9.Оценка"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Evaluating model: Logistic Regression\n",
+ "Train Accuracy: 0.8476, Test Accuracy: 0.8586\n",
+ "Bias: 0.0000, Variance: 0.0000\n",
+ "Train Error (MSE): 732205.3240, Test Error (MSE): 723300.1101\n",
+ "Bias: 0.0000, Variance: 0.0000\n",
+ "Evaluating model: Random Forest\n",
+ "Train Accuracy: 0.8476, Test Accuracy: 0.8586\n",
+ "Bias: 0.0000, Variance: 0.0000\n",
+ "Train Error (MSE): 732205.3240, Test Error (MSE): 723300.1101\n",
+ "Bias: 0.0000, Variance: 0.0000\n",
+ "Evaluating model: SVM\n",
+ "Train Accuracy: 0.8476, Test Accuracy: 0.8586\n",
+ "Bias: 0.0000, Variance: 0.0000\n",
+ "Train Error (MSE): 732205.3240, Test Error (MSE): 723300.1101\n",
+ "Bias: 0.0000, Variance: 0.0000\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.metrics import mean_squared_error, accuracy_score\n",
+ "\n",
+ "# Оценка смещения и дисперсии для классификации и регрессии\n",
+ "def evaluate_bias_variance(model, X_train, y_train, X_test, y_test, task='classification'):\n",
+ " # Прогнозы на обучающих и тестовых данных\n",
+ " y_train_pred = model.predict(X_train)\n",
+ " y_test_pred = model.predict(X_test)\n",
+ "\n",
+ " if task == 'classification':\n",
+ " # Для классификации считаем точность\n",
+ " train_accuracy = accuracy_score(y_train, y_train_pred)\n",
+ " test_accuracy = accuracy_score(y_test, y_test_pred)\n",
+ " print(f\"Train Accuracy: {train_accuracy:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n",
+ " elif task == 'regression':\n",
+ " # Для регрессии считаем среднеквадратичную ошибку (MSE)\n",
+ " train_error = mean_squared_error(y_train, y_train_pred)\n",
+ " test_error = mean_squared_error(y_test, y_test_pred)\n",
+ " print(f\"Train Error (MSE): {train_error:.4f}, Test Error (MSE): {test_error:.4f}\")\n",
+ "\n",
+ " # Для оценки смещения и дисперсии на тестовых данных\n",
+ " bias = np.mean(y_test_pred - y_train_pred[:len(y_test_pred)]) # Смещение: разница между тестом и обучением\n",
+ " variance = np.var(y_test_pred - y_train_pred[:len(y_test_pred)]) # Дисперсия: варьирование прогнозов\n",
+ "\n",
+ " print(f\"Bias: {bias:.4f}, Variance: {variance:.4f}\")\n",
+ "\n",
+ "# Оценим для каждой из моделей\n",
+ "for name, model in best_models.items():\n",
+ " print(f\"Evaluating model: {name}\")\n",
+ " # Для классификации\n",
+ " evaluate_bias_variance(model, X_train_class, y_train_class, X_test_class, y_test_class, task='classification') \n",
+ " # Для регрессии\n",
+ " evaluate_bias_variance(model, X_train_reg, y_train_reg, X_test_reg, y_test_reg, task='regression') \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Конец"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "miivenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
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